AI Anomaly Correlation
An extensive suite of AI and ML models to detect slow-moving, cross-domain anomalies across your entire stack — from silicon to semantic — without pre-configured rules. 100% unsupervised self-driving. FFWD correlate them to surface root causes that siloed monitoring tools can never find.
Complex systems are cross-layer, cross-domain, cross-stack
Splunk sees security logs. Datadog sees APM traces. Network tools see packets. Each one alerts inside its own domain — and misses everything between. The hardest problems are causal chains that propagate vertically through layers and laterally across stacks.
FFWD sees them all simultaneously because the pipeline sits at the aggregation point where telemetry from every layer and every domain converges.
Unsupervised marker auto-discovery, auto-extraction and auto-evaluation
FFWD continuously discovers and extracts markers from raw logs and metrics — features that describe your system’s operational state. An extensive AI/ML toolbox evaluates every marker for anomaly, in real time.
Explicit Marker Examples
Implicit Marker Examples
Root-Cause Advisory, MCP-Native
Anomaly findings, journals, and raw telemetry — delivered directly to your AI agents. Same data layer, queryable by any MCP-compatible agent.
Natural-language root-cause advisory
FFWD synthesises anomaly findings into natural-language reports, powered by your LLM of choice — Claude, GPT, Gemini, Grok, or on-prem.
Each report covers symptoms detected, probable root causes, remedies, and the specific symptomatic log lines as evidence.
Private Deployment
FFWD Anomaly Correlation runs entirely within your environment. On-premises, private cloud, or air-gapped — your telemetry never leaves your security perimeter. No SaaS dependencies. No data sovereignty concerns. An extensive AI and ML toolkit trains and runs locally on your infrastructure.
Multi-tenant architecture lets you run FFWD as private SaaS — serving multiple business units from a single deployment with full data isolation.